System and method for identification of false statements

ABSTRACT

A method of detecting lie detection including the steps of first conditioning the subject to produce an involuntary physiological response triggered when the subject provides false statements; and secondly asking the subject to state whether each of series of statements presented to the subject are true. As the subject answers, the subject is monitored for the conditioned involuntary response. Detecting whether the subject is providing false statements is based on whether the involuntary response is observed during these answers. A system ( 19 ) for lie detection includes a computing device ( 21 ), a controller ( 40 ), and at least one conditioning interface ( 42, 44 ).

BACKGROUND

The present invention relates to a system and method of detecting when asubject is lying by conditioning the subject to produce a uniquephysiological response which is not a naturally occurring physiologicalphenomenon, in contrast to the prior methods which rely on monitoringnatural responses. Specifically, using classical Pavlovian conditioningtechniques and semantic generalization, a unique autonomic response iscreated in test subjects when they are exposed to a true versus a falsestatement (conditioned stimuli). This assures that an independentassessment can be made of whether a given examinee shows the conditionedresponse to known questions and allows quantitative comparisons ofconditioned responses to the critical questions to be compared to thedistribution of conditioned responses shown to known true and knownfalse statements.

Current lie detection methods, based on the polygraph technique, relyupon detecting changes in the physiological characteristics of thesubject. Among the characteristics measured are respiration rate, skinresistivity, blood pressure, and heart rate. One such method is therelevant/irrelevant test (RIT). As the name implies, the subject isasked a series of relevant and irrelevant questions. Measurements of thesubject's physiological characteristics are made while the subject isanswering these questions. If the physiological response to the relevantquestions is greater than to the irrelevant questions, the subject isdeemed to be deceptive. Responses of equivalent size to the two types ofquestions indicate truthfulness.

Another approach is the control question test (CQT). The CQT involves acomparison of responses to relevant questions, to certain controlquestions which are designed to elicit emotional reactions (e.g. “Haveyou ever taken something from someone who trusted you?”). Assuming thateveryone has done the sorts of things asked in the control questions,innocent people should react more strongly to control questions thanrelevant questions. Conversely, dishonest people should react morestrongly to relevant questions than control questions.

Yet another method is the Guilty Knowledge Test (GKT). In the GKT thesubject is asked a series of multiple choice questions, all dealing withfacts with which only those knowledgeable about the crime would befamiliar. The GKT assumes that the guilty individual's recognition ofthe correct multiple choice alternative that contains actual details ofthe crime will lead to stronger physiological responses than toincorrect alternatives.

The technology used to monitor and record physiological measurementsused by the polygrapher is typically a portable field polygraph.Recorded activity includes electrodermal responsitivity (for exampleskin resistance or conductance), monitored from stainless steelelectrodes attached to the fingertips; respiration, recorded frompneumatic belts positioned around the upper chest and abdomen; and a“cardio” channel in which relative changes in blood pressure aredetermined by observing pressure oscillations obtained from a standard,partially inflated sphygmomanometer cuff placed on the subjects arm.Some methods may also record brain activity usingelectro-encephalography to measure P3 brain waves. Records are madeeither mechanically or are digitized and stored in a computer.

Even with control questions, the problem with all three of the methodsdescribed in previous paragraphs is that they rely on monitoring naturalphysiological responses which may fluctuate for reasons other thandeceptive conduct or response to questions by the subject. Otherpotential problems and limitations are outlined in the National ResearchCouncil (2002) report on the scientific validity of the polygraph.Predictably, these methods produce false negatives (i.e. a deception ismissed) and false alarms (subject is not lying, but inquisitor believessubject is). The problem of false positives and false negatives would belessened if a novel autonomic pattern were semantically conditioned totrue statements and the opposite novel autonomic pattern weresemantically conditioned to false statements. These autonomic patternswould be involuntary, innocuous, and visibly undetectable, yet moreaccurate (i.e. lower false alarm rate) and sensitive (i.e. low falsenegative rate) than the traditional approaches described above.

Another problem with these traditional approaches is that they may bevulnerable to the deployment of countermeasures by the subject (NationalResearch Council, 2002). Subjects who are aware that showing a strongerphysiological response to control questions than to relevant questionsis indicative of truthfulness can manipulate their physiology usingcognitive, emotional, or motoric acts to influence their physiologicalresponses to questions. One known countermeasure is increasing breathingby methods such as holding one's breath for 5–20 seconds after answeringa control question. Another known countermeasure is to increase one'sheart rate using methods such as constricting one's anal sphinctermuscle, biting down on the tongue, or thinking exciting thoughts.Because of the effectiveness of such countermeasures, there is a needfor a detectable response to lying that are more difficult to manipulateby the subject during testing.

SUMMARY OF THE INVENTION

The present invention relates to a system and method of detecting when asubject is lying by conditioning the subject to produce a uniquephysiological response which is not a naturally occurring physiologicalphenomenon, in contrast to the prior methods which rely on monitoringnatural responses. Specifically, using classical Pavlovian conditioningtechniques and semantic generalization, a unique autonomic response iscreated in test subjects when they are exposed to a true versus a falsestatement (conditioned stimuli). This assures that an independentassessment can be made of whether a given examinee shows the conditionedresponse to known questions and allow quantitative comparison ofconditioned responses to the critical questions to be compared to thedistribution of conditioned responses shown to known true and knownfalse statements.

One embodiment of the conditioned physiological response is vasomotoractivity—an innocuous and reflexive response to mild heating and coolingof the skin. Because of the way conditioning is accomplished (one siteis conditioned to show vasoconstriction and another is conditioned toshow vasodilation to a true statement, vice versa when presented withfalse statements), the reflexive response pattern is not observablenaturally. That is, there is a near zero baserate for this pattern ofresponse.

Disclosed is a system and the system's method of use which maybedescribed as generally involving three stages. First, semanticconditioning is used to produce a bidirectional vasomotor/physiologicalresponse that otherwise would not occur (zero baserate). Second, aconditioning and testing procedure is implemented based on semanticgeneralization. Third, data from this second stage is used to assess thetruth or falsehood of intermittent test statements based on an observedpattern of conditioned responses. The entire conditioning, testing, andassessment procedure may be fully automated, thereby standardizingtesting and avoiding confounding influences of examiner expectations orexaminer/examinee interactions or rapport.

The differential conditioning used to form novel and distinctivepatterns of physiological response makes it difficult forcountermeasures to be used by the subject that are not detected duringthe presentation of known control questions in the second phase of thestudy. Moreover, the conditioned response is not visible to the nakedeye. This is advantageous over a visible response such as training thesubject to blink, which would result in the subject blinking tofalsehoods after leaving the examination environment. In contrast,vasoconstriction or vasodilation is not obvious.

An aspect of this invention is a system for detecting false statementsmade by a subject which includes a programmed computing device, anoutput device configured to receive data from the programmed computingdevice. Also included is a controller configured to be operated by theprogrammed computing device which controls at least one conditioninginterface. Each conditioning interface includes a physiologicalmeasuring device and an attachment structure for attaching theconditioning device to a selected body part of a test subject.

Also disclosed is a method of using the above system. This method of useincludes first attaching the conditioning interfaces to the subject.Next, the subject is conditioned to produce an involuntary physiologicalresponse when the subject states a false statement. Further, the methodincludes subjecting the subject to a testing stage which involvespresenting the subject with a series of questions and recording thesubject's answers to the series of questions while contemporaneouslyrecording data related to the involuntary physiological response. Afterthe data is collected, it is analyzed to determine whether the subjectis providing false statements.

The step of conditioning the subject to produce an involuntaryphysiological response described above involves: (a) exposing thesubject to a statement that is true statement to the subject; (b)stimulating a first body part of the subject with a first stimulus andcontemporaneously stimulating a second body part of the subject with asecond stimulus to obtain a first conditioned physiological responsewhich is designated the true response; and (c) repeating a cycle ofsteps a–b.

For the method described above, exposing the subject to a statement maybe displaying the statement on a screen. In the alternative, exposingthe subject to a statement may be by producing the statement audibly forthe subject to hear.

Also for the method described above, the physiological response is avasoconstriction of blood vessels in the first body part, andvasodilation of the blood vessels in the second body part. In thealternative, the physiological response may be a blinking of an eye.Further, for the method described above, the first stimulus and secondstimulus may be only applied during a subset of the cycle.

Finally, the invention includes a method of detecting lie detection.First, the subject is conditioned to produce an involuntaryphysiological response triggered when the subject provides falsestatements. Next, the subject is asked to state whether each of a seriesof statements presented to the subject are true. As the subject answers,the subject is monitored for the conditioned involuntary response.Detecting whether the subject is providing false statements is based onwhether the involuntary response is observed during these answers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified diagrammatic view of a system for the detectionof false statements;

FIG. 2 is a circuit schematic of a first portion of a circuit thatcontrols the application of stimuli to a subject being tested;

FIG. 3 is a circuit schematic of a second portion of a circuit thatcontrols the application of stimuli to a subject being tested;

FIG. 4 is a prospective view of one embodiment of a conditioninginterface with a clip attachment structure;

FIG. 5 is the conditioning interface of FIG. 4 shown attached to asubject's ear;

FIG. 6 is a prospective view of another embodiment of a conditioninginterface with a strap attachment structure;

FIG. 7 shows two conditioning interfaces of FIG. 6 attached to asubject's right and left index fingers;

FIG. 8 shows an example of sequential screens used to condition aphysiological response;

FIG. 9 shows comparative charts of conditioned versus observedphysiological responses.

DETAILED DESCRIPTION OF THE INVENTION

With reference to the figures, FIG. 1 shows the components of a system19 for detection of false statements. In one embodiment, the system 19is implemented using a computing system 21 such as a programmed generalpurpose computer which desirably includes a processor 22, memory 24, astorage device 28 such as hard drive, tape drive, or floppy disk, and asoftware module 27 stored on the storage device 28. In such anembodiment, the system 19 would also include one or more output devices30 such as a monitor or printer, and one or more input devices 32 suchas, for example, a keyboard, mouse, touch display or voice control.

The processor 22 is programmed to operate by the software module 27. Theterm “module” referenced in this disclosure is meant to broadly covervarious types of software code including routines, functions, objects,libraries, classes, members, packages, procedures, methods, or lines ofcode together performing similar functionality to these types of coding.

In another embodiment, the computing system 21 may be an integrateddevice such as a laptop computer, tablet PC, or handheld device such asa personal digital assistant. In such an embodiment, one or more thecomponents mentioned above may be built into the computing system 21. Itis also envisioned that the present system 19 can be embodied on aninternet based system for use by multiple users simultaneously andremote storage and retrieval of information.

The system 19 also includes a controller 40 for operating two or moreconditioning interfaces 42, 44. The controller 40 is controlled by thecomputing system 21. Circuit schematics of portions of one embodiment ofcontroller 40 are shown in FIGS. 2 and 3. The circuit components shownin FIGS. 2 and 3 are common components available from any number ofsuppliers and therefore are not described in detail for the sake ofbrevity.

The conditioning interfaces 42, 44 includes an industry standardthermoelectric cooler (“TEC”) such as the TEC available from MarlowIndustries. The TEC cools when electric current is passed in onedirection and heats when the current flow is reversed. The direction andamount of current passing through the TEC is controlled by thecontroller 40. The TEC is attached to an attachment structure forattachment to a selected body part of a human test subject. Theattachment structure may take various forms depending on the selectedbody part including a clip for attachment to an ear or a strap forattachment to a wrist, hand, arm, or leg.

The conditioning interface 42, 44 is also fitted with a physiologicalmeasuring device (“PMD”); such as a plethysmograph, for measuring bloodflow at the selected body part. A plethysmograph is an instrument thatmeasures variations in the size of an organ or body part on the basis ofthe amount of blood passing through or present in the part. In oneembodiment, a photoplehtysmograph may be utilized. Other physiologicalmeasuring devices, such as devices to record eye blinking, may also beutilized.

Referring now to FIG. 4, an embodiment of the conditioning interface 44Auses a clip attachment structure 60. Coupled between the arms 62, 64 ofthe clip 60 is the PMD 66 positioned adjacent to a TEC 68. FIG. 5 showsconditioning interface 44A attached to a subject's ear.

In another embodiment as shown in FIG. 6, a conditioning interface 44Buses a strap attachment structure 60B. A PMD 66B is positioned generallycoplanar to the strap 60B when engaged with a body part, such as the tipof an index finger as shown in FIG. 7.

In use, the conditioning interfaces 42, 44 are attached to selected bodyparts of a test subject. The computing system 21 controls the controller40 which sends a desired amount and direction of current to eachconditioning interface 42, 44. The TEC located within each conditioninginterface 42, 44 heats or cools the subject's skin which results invasoconstriction or vasodilation of the capillaries in that region. Thislocally alters blood flow. The PMDs provide blood flow data which issent back to the computing system 21 for storage and analysis.

In an embodiment using a plethysmograph as the PMD, data is collectedusing photoplethysmographic method. Photoplethysmography produces arelative measure of the amount of blood perfusing the underlying tissuebased on the amount of light emitted from an LED that is reflected backand measured by a photoelectric cell. The greater the perfusion of theunderlying tissue, the greater the absorption of the emitted light andthe less the refracted light to be detected at the photoelectric cell.The units of measurement in photoplethysmography are arbitrary. Althoughthese measures are generally stable at a recording site within asession, they can vary dramatically across sites or whenever a sensor isrepositioned. Therefore, the amplitude functions (f(xi)) constitutingthe time series within a recording site can be expressed as a Z-score,where Z=[f(xi)−Mean(f(xi))]/Standard Deviation. This operation producestime series across recording sites that are on equivalent scales. Thatis, they are normalized to a mean of zero and a standard deviation ofone. The time series from the selected recording sites can be subtractedfrom one another to produce a different (or composite) waveform usefulin the analyzing stage.

The system 19 is operated using the following method. This method may begenerally divided into three stages. In a first stage, hereinafterreferred to as “training stage”, a subject is conditioned to produce apattern of involuntary responses whenever a true statement is displayedto the subject and to produce a reverse pattern of involuntary responseswhenever a false statement is displayed. The second stage, hereinaftercalled the “testing stage,” is to display statements to the subject, theanswers to some of which are known to the examiner (control statements)and the answers to which some are not known (test statements). The thirdstage, hereinafter referred to as an “analyzing stage” involvesanalyzing data collected during the testing stage to verify differentialconditioned responses to (known) true and false statements throughoutthe testing phase, and assess the truth or falsehood of (intermittent)test statements based on the similarity of the observed pattern ofconditioned responses to those associated with known true and falsestatements.

Preliminarily, the conditioning interfaces 42, 44 are attached to twobody parts of the test subject. Although this embodiment of the methodutilizes two body parts, it is envisioned that additional conditioninginterfaces may be used as well. In one embodiment, the selected bodyparts are on opposite sides of the body. The selected bodyparts maybethe same on both sides of the subject's body, for example, the left andright index finger, or maybe different body parts on opposite side's ofthe subject's body, such as the left index finger and the right arm. Inanother embodiment, the body parts may be on the same side of the body.In another embodiment, one conditioning interface may configured toaffect two closely spaced body parts, for example adjacent parts of ahand, arm, or leg. The subject is positioned to communicate with thesystem 19 using the input device 32 and output device 30. Forsimplicity, the further description below describes the subject viewingoutput using a computer screen.

In the first stage, the subject views a series of statements on thecomputer screen. Each statement has two parts, the first part beingdisplayed for a sufficient time for the user to the read first part ofthe statement. The screen then clears, and the second statement isdisplayed, again for a sufficient time for the subject to read andconsider the second part of the statement. For example, a displayedfirst part of a statement may be “I don't like being . . . ” followed bya displayed second part of a statement being “honored”. Another examplemay be “I support” as a displayed first part of a statement, followed bythe word “terrorism” as a displayed second part of a statement.Optionally, to assure that subjects are paying attention, four secondsafter the word completing the sentence appears, a subject may beinstructed to input into the system 19 using the input device 32,whether the completion was true or false. In the alternative, thesubject may tell a person supervising the method (the “supervisor”),whether the completion was true or false. The instructions to give sucha response may be in the form of displayed instructions on the computerscreen, or may be given the supervisor. Display of the statement partsis repeated until a pre-programmed number of statements has beendisplayed or until the supervisor concludes the testing phase.

During the presentation of the sentence completion, but prior to theverbal or inputted response (conditioned stimuli—CS), the system 19applies temperature stimulation using the conditioning interfaces 42, 44(unconditioned stimuli—UCS). In an embodiment where two body parts areused, one body part is heated while the other is cooled for aphysiological response designated as the “true response.” For the “falseresponse,” the heating and cooling is reversed. This temperatureapplication is performed during less than all of the of the displayedstatements, hereinafter referred to as “trials”. For example, thetemperature application may be performed during only 80% of the trials.This partial reinforcement is used to avoid the problem of extinction ora reduction in responding. Thus, the perceived veracity of the wordcompletion is conditioned to the temperature change, not the verbalresponse.

Another embodiment of the training stage makes use of a series ofstatements about objects or events. A first screen is displayed with afactual statement. A second screen is displayed asking about the firststatement. The subject is instructed, either verbally by the supervisoror through displayed instructions, to answer all questions “NO.”Differential heating and cooling occurs depending upon whether the trialis true or false. For example, the left hand may cooled and the righthand heated for a true response, and the left hand heated and right handcooled for a false response. In this manner, the autonomic response istrained. An example is shown in FIG. 8. In this example, temperaturechanges are administered when the “4 pm” appears on the screen momentsafter “Was your appointment at” appears.

In the testing phase, the ratio of temperature-reinforced trials isreduced to 50%. Additionally, true and false sentence completions areadded that had not been previously introduced to assess the effect ofthe conditioning trials on novel stimuli. Approximately forty trials arepresented in each phase, although this number can increase or decreasedepending on the number of test questions to be examined. Vasomotorresponse data is continuously recorded from the plethysmographs in theconditioning interfaces 42, 44 (unconditioned and conditionedresponses—UR and CR) and sent to the computing device 21 for analysis.The UCS follows less than 100% of the control statements and 0% of thetest statements, and each of these statements is presented multipletimes to allow ensemble averaging to minimize spontaneous physiologicalnoise.

In the analysis stage, the collected data may be processed to improveconfidence in the readings, through statistical methods such as ensembleaveraging, normalizing, and filtering. Where two body parts are used,the responses from the first body part are subtracted from the secondbody part to reduce the oscillatory activity due to cardiac output anddifferential signal sensitivity across recording sites.

During data collection, the signal from each recording site may befiltered using a filtering device, such as a second order Butterworthband-pass filter, with a pass band of appropriate range, such as between0.05 and 0.4 Hz. Following the filtering, non-trial data periods wereremoved and the time series representing photoplethysmographic activityduring each trial at each recording site are centered by subtracting themean value from the time series. This results in a time series ofn-measurements at each recording site for each trial.

Although a variety of analysis methods maybe used, one analysis methoduses the collected data to produce a composite wave form. The compositewaveform is then re-centered by taking the mean of the two secondspreceding the stimulus presentation and subtracting it from the entirecomposite waveform on a trial by trial basis. Next, composite waveforms(or the component individual time series within site if that is themeasure of interest) within each condition (e.g., the UCR trials, trialson which the known answer is true, trials on which the known answer isfalse, trials in which a specific critical question was asked) from eachsite are then ensemble averaged. Specifically, a time series ofn-measurements on m-trials produce a matrix of m*n size. The ensembleaveraged time series is achieved by taking the mean of the measurementat each time point in the time series, resulting in the reduction of them*n matrix to an n-vector or mean time series for that condition andsite. Ensemble averages are produced for all conditions. This processingmethodology minimizes noise, particularly the noise due to cardiacactivity that produces an oscillatory signal that is relatively similarand time-synched between the two fingers and thus raised the signal tonoise ratio of the data substantially.

The computing system 21 is used to calculate ensemble averages for eachknown control question, with the number of trials ensemble averagedbeing constant across all control questions and test questions, and maybe used to perform the calculations above for analyzing the waveforms.

The conditioning and testing stages are typically performed in the samesession. The method has another advantage in that the conditioning stayswith the subject for an extended period of time after the session,allowing for further subsequent examination without having to repeat theconditioning procedures.

The computing system 21 compares vasodilatation/constriction associatedwith known false sentence completions versus thevasoconstriction/dilation associated with known true sentencecompletions, and distributions of response patterns are specified withinsubjects. These distributions are then used to evaluate the fit betweeneach and the ensemble averaged conditioned vasomotor patterns observedin response to test questions. Optionally these data may be used toproduce receiver operating curves (ROCs) based on observed responses totraining questions. Using known signal detection theory, ROCs may thenbe used to optimize and evaluate the classification of truths and falsestatements.

The computer system 21 may be fitted with a communications port forreceiving data from the conditioning interfaces 42, 44. In oneembodiment, data so received may be stored and imported into a softwaremodule specifically written to perform data analysis. An example ofsoftware code for receiving the data is shown below in Example 2. Inanother embodiment, the data may be imported into a spreadsheet, such,as Excel from Microsoft, for manipulation, comparison, and reductiveanalysis. This filtered data may be plotted in a chart form for easyvisual comparison. The program module 27 operates the processor toperform these calculations to allow comparison of the normalizedobserved patterns with the patterns instilled during the conditioningphase. Where the observed pattern matches the instilled pattern for afalse response, the subject is giving false statements. An example ofprintout comparing the conditioned versus observed results is shown inFIG. 9.

EXAMPLE 1 Differentiation of True and False Statements using SemanticConditioning Experiment

Twelve male college students served as subjects. Following obtaininginformed consent and the completion of several questionnaires, twoconditioning interfaces were attached to the left and right indexfingers of the subjects using hook and loop fasteners. Theseconditioning interfaces consisted of photoplethysmographs integratedinto thermoelectric cooling devices. These devices and their computerinterface allowed control of the heating and cooling of the fingersduring the presentation of text information on a computer screen.

The conditioning procedures were presented in two phases. The firstphase required that subjects view a series of statements on the computerscreen. The statements were presented in two parts such as “I don't likebeing” followed by the word “honored” and “I support” followed by theword “terrorism.” To assure that subjects were paying attention, fourseconds after the word completing the sentence appeared, subjects wereinstructed to say if the completion was true or false. During thepresentation of the sentence completion but prior to the verbal response(conditioned stimuli—CS), temperature stimulation (unconditionedstimuli—UCS) was applied on 80% of the trials. Thus, the perceivedveracity of the word completion was conditioned to the temperaturechange, not the verbal response. In the second phase, the ratio oftemperature reinforced trials was reduced to 50%. Additionally, true andfalse sentence completions were added that had not been previouslyintroduced to assess the effect of the conditioning trials on novelstimuli. Forty trials were presented in each phase. Vasomotor responseswere continuously recorded from the two plethysmographs (unconditionedand conditioned responses—UR and CR) and the record was marked by thecomputer administering the textual stimuli to indicate the presentationof a completion and whether it was true or false.

The signals from the plethysmographs were normalized, filtered, and theresponses from the right finger were subtracted from the left finger.Such processing reduced the oscillatory activity due to cardiac outputand potentiated the differences between the two fingers providing forthe clear comparison of vasodilatation/constriction associated withfalse sentence completions versus the vasoconstriction/dilationassociated with true sentence completions. Plethysmograph responses ofthe 12 subjects during the training trials where temperature inductionswere associated with true and false statements (CS/UCS pairings) wereplotted and averaged. Statistical comparison of these waves indicatedthat they differed significantly from each other (p=0.01).

EXAMPLE 2 Software Code for Receiving and Analyzing Data fromConditioning Interfaces—Four Modules

A. Module 1—“pddVNewOddE.m” is a MATLAB R12 script to analyze data fromconditioning and test series. This script provides the followingfunctionality: (1) filters the total record waveforms for the first andsecond body part; (2) centers the data by subtracting the mean of thewhole record; (3) equates the amplitudes of each channel by multiplyingeach channel by the inverse mean of the absolute value of that channeldata; and (4) converts the record into z-scores. Data is recorded intoindividual trials, maintaining data from left and right channels andadding a difference channel that is the result of subtracting the leftand right channels. A baseline correction maybe applied to thesubtracted data channel.

Next, the script performs a separation of the epoched trials into setsby their trial type, as determined by the trigger channel data that hasbeen maintained from the beginning of the data processing. The meansubtracted wave from for each trial type is then calculated. The data isthen plotted with a option to save the data in MATLAB or ASCII format.Specific variable names such as “Right finger” and “Left finger” areused for illustrative purposes only, as other body parts may be used:

B. MODULE 2—“PLOTPAIRS” is a function called by MeanByType.m to plot themean waveforms, on a subject-by-subject basis, for each trial type,plotting the two conditions on the same axes, and the twoconditioning/training conditions on another set of axes:

function [temp] = MeanByType(Lies,Truths,SaveFlag,Type,UseTypeFlag)CenterWaves = 0; set(0,‘DefaultAxesLineStyleOrder’,‘-|-.|--|:’); ifnargin == 2  Type = ”  SaveFlag = 0;  UseTypeFlag = 0; end if nargin ==3  Type = ”;  UseTypeFlag = 0; end if nargin == 4  UseTypeFlag = 1; endif UseTypeFlag == 0  Type = ”; end %center if (CenterWaves)  [Sbjs,temp] = size(lies);  for i = 1:Sbjs  Lies(i,:)=Lies(i,:)−mean(Lies(i,1:600)); %center  Truths(i,:)=Truths(i,:)−mean(Truths(i,1:600));  end endMinVal=min(min([Lies; Truths])); MaxVal=max(max([Lies; Truths]));MaxSize=max(max([size(Lies) size(Truths)])); V=[ ]; [TruthSize, temp] =size(Truths); [LieSize, temp] = size(Lies); fa1 =figure(‘name’,strcat(‘PlotBySbj:’,Type,‘1’));title(strcat(‘PlotBySbj:’,Type,‘1’));SaveName=strcat(‘PlotBySbj_’,Type,‘1’); for i=1:(TruthSize/2) subplot(2,TruthSize/4,i); %  plot([Lies(i,:);Truths(i,:)]’); plot(Lies(i,:),‘k:’);  hold on  plot(Truths(i,:),‘b’);  hold off axis([0 MaxSize MinVal MaxVal]); title(strcat(‘SBJ:’,int2str(i),‘:’,Type))  xlabel(strcat(‘Samples(‘,int2str(200),’/second)’))  ylabel(‘PPG Output (volts)’)  hold on plot([600 600], [MinVal MaxVal],‘r-.’)  hold off  a=i; endsaveas(fa1,strcat(SaveName,‘.fig’)) saveas(fa1,strcat(SaveName,‘.jpg’))fa2 = figure(‘name’,strcat(‘PlotBySbj:’,Type,‘2’));title(strcat(‘PlotBySbj:’,Type,‘2’));SaveName=strcat(‘PlotBySbj_’,Type,‘2’); for i=1:(TruthSize/2) subplot(2,TruthSize/4,i); %  plot([Lies(i+a,:);Truths(i+a,:)]’); plot(Lies(i+a,:),‘k:’);  hold on  plot(Truths(i+a,:),‘b’);  axis([0MaxSize MinVal MaxVal]);  title(strcat(‘SBJ:’, int2str(a+i),‘:’,Type)) xlabel(strcat(‘Samples (‘,int2str(200),’/second)’))  ylabel(‘PPG Output(volts)’)  hold on  plot ([600 600], [MinVal MaxVal],‘r-.’)  hold offend saveas(fa2,strcat(SaveName,‘.fig’))saveas(fa2,strcat(SaveName,‘.jpg’))

C. MODULE 3—“MeanbyType” is a function that takes the data file outputby pddVNewOddE.m and calculates the ensemble means for a set ofsubjects:

  function [temp] = MeanByType(SubjIds,DVs)   %should PlotPairs be toldto print the type info?   UseTypeFlag = 0;   %Should the waveforms be  CenterWaves = 0;   %set defaul for DataVars   DataVars = ‘FCEqW’   ifnargin == 2    DataVars=DVs;   end   DVString = DataVars;   DataVars =strcat(‘-’,DataVars);   FileStart = ‘VAR9SBJ_’;   FileEnd =strcat(DataVars,‘.Ensemble.mat’);   disp(SubjIds)   [nsubjtmp]=size(SubjIds);   sr = 200;   %Diff_MeanLies = [ ];  %Diff_MeanTruths = [ ];   %Diff_MeanStimLies = [ ];  %Diff_MeanStimTruths = [ ];   L_MeanLies = [ ];   L_MeanTruths = [ ];  L_MeanStimLies = [ ];   L_MeanStimTruths = [ ];   R_MeanLies = [ ];  R_MeanTruths = [ ];   R_MeanStimLies = [ ];   R_MeanStimTruths = [ ];  BaseStarts=[ ];   WinDurs=[ ];   for i=1:nsubj    s =load(strcat(FileStart,num2str(SubjIds(i,:)),FileEnd));   Diff_MeanLies(i,:) = s.M_Diff_BFNS';    Diff_MeanTruths(i,:) =s.M_Diff_BTNS';    Diff_MeanStimLies(i,:) = s.M_Diff_BFS';   Diff_MeanStimTruths(i,:) = s.M_Diff_BTS';    L_MeanLies(i,:) =s.Mean_Left_FalseNoUCS';    L_MeanTruths(i,:) = s.Mean_Left_TrueNoUCS';   L_MeanStimLies(i,:) = s.Mean_Left_FalseUCS';    L_MeanStimTruths(i,:)= s.Mean_Left_TrueUCS';    R_MeanLies(i,:) = s.Mean_Right_FalseNoUCS';   R_MeanTruths(i,:) = s.Mean_Right_TrueNoUCS';    R_MeanStimLies(i,:) =s.Mean_Right_FalseUCS';    R_MeanStimTruths(i,:) =s.Mean_Right_TrueUCS';    BaseStarts(i,:)=s.basestart';   WinDurs(i,:)=s.windur';    clear s   end   %center   if (CenterWaves)   for i = 1:nsubj     Diff_MeanLies(i,:) =Diff_MeanLies(i,:)−mean(Diff_MeanLies(i,1:400));    Diff_MeanTruths(i,:)       =Diff_MeanTruths(i,:)−mean(Diff_MeanTruths(i,1:400));     Diff_MeanStimLies(i,:)     =Diff_MeanStimLies(i,:)− mean(Diff_MeanStimLies(i,1:400));    Diff_MeanStimTruths(i,:)=Diff_MeanStimTruths(i,:)−mean(Diff_MeanStimTruths(i,1:400));     L_MeanLies(i,:) =L_MeanLies(i,:)−mean(L_MeanLies(i,1:400));     L_MeanTruths(i,:)=L_MeanTruths(i,:)−mean(L_MeanTruths(i,1:400));     L_MeanStimLies(i,:)     =L_MeanStimLies(i,:)− mean(L_MeanStimLies(i,1:400));    L_MeanStimTruths(i,:)=L_MeanStimTruths(i,:)−mean(L_MeanStimTruths(i,1:400));     R_MeanLies(i,:) =R_MeanLies(i,:)−mean(R_MeanLies(i,1:400));     R_MeanTruths(i,:)=R_MeanTruths(i,:)−mean(R_MeanTruths(i,1:400));     R_MeanStimLies(i,:)      =R_MeanStimLies(i,:)− mean(R_MeanStimLies(i,1:400));    R_MeanStimTruths(i,:)=R_MeanStimTruths(i,:)−mean(R_MeanStimTruths(i,1:400));    end   end  save(strcat(‘MeanByType’,DataVars,‘.mat’),‘BaseStarts’,‘WinDurs’,‘R_*’,‘Diff_*’,‘L_*’);   MeanWinDur=mean(WinDurs);   MeanBaseStart=mean(BaseStarts);   f1= figure (‘name’,strcat(DataVars,‘ − Grand Means’));  title(strcat(‘PDD_NEW_ODD’,DataVars, ‘Grand Means’));  MinVal=min(min([mean(Diff_MeanLies);   mean(Diff_MeanTruths);mean(Diff_MeanStimLies); mean(Diff_MeanStimTruths)]));  MaxVal=max(max([mean(Diff_MeanLies);   mean(Diff_MeanTruths);mean(Diff_MeanStimLies); mean(Diff_MeanStimTruths)]));  MaxSize=max(max([size(Diff_MeanLies)    size(Diff_MeanTruths)size(Diff_MeanStimLies) size(Diff_MeanStimTruths)]));   AxisToSet=[0MaxSize MinVal MaxVal];   subplot(1,2,2)   hold on  ys=[mean(Diff_MeanLies(:,:)); mean(Diff_MeanTruths(:,:))];   %To plotvertical line at the onset of the stem completion, not the UCS  VPlotStart = MeanBaseStart + 200   disp(‘size(ys)=’)   disp(size(ys))  %plot(ys)  %plot([mean(Diff_MeanLies(:,:));mean(Diff_MeanTruths(:,:))]’);  plot(mean(Diff_MeanLies(:,:)),‘k:’);  plot(mean(Diff_MeanTruths(:,:)),‘b’);   axis([0 MaxSize MinValMaxVal]);   V = axis;   plot([VPlotStart, VPlotStart],[V(1,3),V(1,4)],‘r-.’)   hold off   title(‘Mean CR’)  xlabel(strcat(‘Samples (‘,int2str(sr),’/second)’))   ylabel(‘PPGOutput (volts)’)   subplot(1,2,1)   hold on   axis([0 MaxSize MinValMaxVal]);  %plot([mean(Diff_MeanStimLies(:,:));mean(Diff_MeanStimTruths(:,:))]’)  plot(mean(Diff_MeanStimLies(:,:)),‘k:’);  plot(mean(Diff_MeanStimTruths(:,:)),‘b’);   axis([0 MaxSize MinValMaxVal]);   title(‘Mean UCR’)   xlabel(strcat(‘Samples(‘,int2str(sr),’/second)’))   ylabel(‘PPG Output (volts)’)   V = axis;  plot([VPlotStart,VPlotStart], [V(1,3),V(1,4)],‘r-.’)  plot([MeanBaseStart,MeanBaseStart], [V(1,3),V(1,4)],‘m:’)   hold off  saveas(f1,strcat(‘GrandMeans’,DataVars,‘.jpg’))  saveas(f1,strcat(‘GrandMeans’,DataVars,‘.fig’))   TYPE = ”;   ifUseTypeFlag    TYPE=strcat(‘(‘,DVString,’)’);   end  PlotPairs(Diff_MeanStimLies,Diff_MeanStimTruths,1,strcat(TYPE,‘UCR’));   PlotPairs(Diff_MeanLies,Diff_MeanTruths,1,strcat(TYPE,‘CR’));

D. MODULE 4—“CalcWindowSums” is a function that takes the data output byMeanByType.m script on a selected time period and calculates the meanfor each subjectt, in each condition over that time period:

  function y = CalcWindowSums(DataVars,WinStart,WinEnd) %DataVars =‘-FCEqZW’   if nargin<2    error(‘Too few inputs’);   elseif nargin>3   error(‘Too many arguments’);   end    s =load(strcat(‘MeanByType-’,DataVars,‘.mat’));   MeanLies =s.Diff_MeanLies;   MeanTruths = s.Diff_MeanTruths;   MeanStimLies =s.Diff_MeanStimLies;   MeanStimTruths = s.Diff_MeanStimTruths;   clears;   if nargin==3    SumMeanTruths =sum(MeanTruths(:,WinStart:WinEnd),2);    SumMeanLies =sum(MeanLies(:,WinStart:WinEnd),2);    SumMeanStimTruths =sum(MeanStimTruths(:,WinStart:WinEnd),    2);    SumMeanStimLies =sum(MeanStimLies(:,WinStart:WinEnd),2);    SumUCR(:,1)=SumMeanTruths ;   SumUCR(:,2)=SumMeanLies ;    SumUCR(:,3)=SumUCR(:,1)−SumUCR(:,2) ;   SumCR(:,1)=SumMeanStimTruths ;    SumCR(:,2)=SumMeanStimLies ;   SumCR(:,3)=SumCR(:,1)−SumCR(:,2) ;    elseif nargin==2   SumMeanTruths = sum(MeanTruths(:,WinStart:end),2);    SumMeanLies =sum(MeanLies(:,WinStart:end),2);    SumMeanStimTruths =sum(MeanStimTruths(:,WinStart:end),2);    SumMeanStimLies =sum(MeanStimLies(:,WinStart:end),2);    SumUCR(:,1)=SumMeanTruths;   SumUCR(:,2)=SumMeanLies;    SumUCR(:,3)=SumUCR(:,1)−SumUCR(:,2);   SumCR(:,1)=SumMeanStimTruths;    SumCR(:,2)=SumMeanStimLies;   SumCR(:,3)=SumCR(:,1)−SumCR(:,2);    end   save(strcat(‘MeanSums-’,DataVars,int2str(WinStart),‘to’,int2str(WinEnd),‘.mat’),‘Sum*’,‘Win*’);  save(strcat(‘SumUCR-’,DataVars,int2str(WinStart),‘to’,  int2str(WinEnd),‘.txt’), ‘SumUCR’, ‘-ascii’, ‘-double’, ‘-tabs’);  save(strcat(‘SumCR-’,DataVars,int2str(WinStart),‘to’,  int2str(WinEnd),‘.txt’), ‘SumCR’, ‘-ascii’, ‘-double’, ‘-tabs’)

While a embodiments of the invention are shown and described, it isenvisioned that those skilled in the art may devise variousmodifications and equivalents without departing from the spirit andscope of the disclosure as recited in the following claims.

Documents Cited

National Research Council. (2002). The Polygraph and Lie Detection.Report by the Committee to Review the Scientific Evidence on thePolygraph. Division of Behavioral and Social Sciences and Education.Washington, D.C.: The National Academies Press.

1. A system for detecting false statements made by a subject, the systemcomprising: (a) a programmed computing device, the programmed computingdevice including a software module operative to; (i) condition a subjectto produce an involuntary physiological response pattern which is not anaturally occurring physiological response, when the subject states afalse statement, and an opposite response pattern to a true statement;and to A. present the subject with a plurality of questions, the answerto some of which are known in advance; B. record the subject's answersto the plurality of questions while contemporaneously recording datarelated to the involuntary physiological responses; and (ii) analyze therecorded data to determine whether the subject is providing falsestatements by comparing conditioned responses to the statements to thedistribution of conditioned responses shown to known true and knownfalse statements; (b) an output device configured to receive data fromthe programmed computing device; (c) a controller configured to beoperated by the programmed computing device; (d) at least oneconditioning interface controllably operated by the controller, eachconditioning interface further comprising: i. an attachment structurefor attaching the conditioning interface to a selected body part of thesubject; and ii. a physiological measurement device.
 2. A method ofusing a system for detecting false statements made by a subject, thesystem comprising a programmed computing device, an output device, acontroller, and at least one controlling conditioning interface, themethod comprising the steps of: (a) attaching at least one conditioninginterface to the subject; (b) conditioning the subject to produce aninvoluntary physiological response which is not a naturally occurringphysiological response pattern when the subject states a falsestatement, and an opposite response pattern to a true statement; (c)subjecting the subject to a testing stage comprising the steps of: i.presenting the subject with a plurality of questions which are answeredby statements, the answers to some of which are known in advance; ii.recording the subject's answers to the plurality of questions whilecontemporaneously recording data related to the involuntaryphysiological response; and (d) analyzing the recorded data to determinewhether the subject is providing false statements by comparingconditioned responses to the statements, to the distribution ofconditioned responses shown to known true and known false statements. 3.The method of claim 2, wherein the step of conditioning the subject toproduce an involuntary physiological response is comprised of thefollowing steps: (a) exposing the subject to a statement that is a truestatement to the subject; (b) stimulating a first body part of thesubject with a first stimulus and contemporaneously stimulating a secondbody part of the subject with a second stimulus to obtain a firstphysiological response, the first physiological response beingdesignated as a true response; (c) repeating a cycle of steps a–b. 4.The method of claim 3, comprising a further step between step b and stepc, wherein the further step is stimulating the first body part withsecond stimulus and the second body part with the first stimulus toobtain a second physiological response, the second physiologicalresponse designated as a false response.
 5. The method of claim 3,wherein exposing the subject to a statement is displaying the statementonscreen.
 6. The method of claim 3, wherein exposing the subject to astatement is by producing the statement audibly for the subject to hear.7. The method of claim 3, wherein the first physiological response is avasoconstriction of blood vessels in the first body part, andvasodilation of the blood vessels in the second body part.
 8. The methodof claim 4, wherein the first physiological response is avasoconstriction of blood vessels in the first body part, andvasodilation of the blood vessels in the second body part and the secondphysiological response is vasoconstriction of blood vessels in thesecond body part, and vasodilation of the blood vessels in the firstbody part.
 9. The method of claim 3, wherein the first physiologicalresponse is a blinking of an eye.
 10. The method of claim 3, wherein thefirst stimulus and second stimulus is only applied during a subset ofthe cycle.
 11. A method of detecting false statements made by a subjectcomprising the steps of: (a) conditioning the subject to produce aninvoluntary physiological response which is not a naturally occurringphysiological response pattern, when the subject provides falsestatements, and an opposite response pattern to a true statement; (b)asking the subject to state whether a series of statements presented tothe subject are true and monitoring for the involuntary response duringthe subject's answers; and (c) detecting whether the subject isproviding false statements based on whether the involuntary responseassociated with a false statement is observed during the subject'sanswers.
 12. The method of claim 11, wherein the step of conditioningthe subject to produce an involuntary physiological response iscomprised of the following conditioning steps: (a) exposing the subjectto a statement that is a true statement to the subject; (b) stimulatinga first body part of the subject with a first stimulus andcontemporaneously stimulating a second body part of the subject with asecond stimulus to obtain a first physiological response, the firstphysiological response being designated as a true response; (c)repeating a cycle of conditioning steps a–b.
 13. The method of claim 12,comprising a further step in the cycle of stimulating the first bodypart with second stimulus and the second body part with the firststimulus to obtain a second physiological response, the secondphysiological response designated as a false response.
 14. The method ofclaim 12, wherein exposing the subject to a statement is displaying thestatement onscreen.
 15. The method of claim 12, wherein exposing thesubject to a statement is by producing the statement audibly for thesubject to hear.
 16. The method of claim 12, wherein the firstphysiological response is a vasoconstriction of blood vessels in thefirst body part, and vasodilation of the blood vessels in the secondbody part.
 17. The method of claim 12, wherein the first physiologicalresponse is a vasoconstriction of blood vessels in the first body part,and vasodilation of the blood vessels in the second body part and thesecond physiological response is vasoconstriction of blood vessels inthe second body part, and vasodilation of the blood vessels in the firstbody part.
 18. The method of claim 12, wherein the first physiologicalresponse is a blinking of an eye.
 19. The method of claim 12, whereinthe first stimulus and second stimulus is only applied during a subsetof the cycle.